Context-Aware Sentiment Analysis for Technical Support Communications
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Solution Overview
Problem
Conventional methods for determining customer sentiment in technical support contexts, such as customer surveys and automated sentiment analysis, face challenges in accuracy and reliability, particularly in formal interactions, leading to incomplete and biased feedback.
Innovation Solution
A system and method using natural language processing (NLP) to analyze communications between customers and support agents, filtering out technical syntax, applying trained models to identify sentiment features, and combining scores with metadata and context to provide real-time, granular sentiment metrics, including Sentiment Score calculations for various dimensions like customer, product, and agent experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional customer surveys are used to determine sentiment, then feedback can be collected, but accuracy and reliability deteriorate due to incomplete and biased feedback in formal technical support interactions
Solution Approach 1:
The patent replaces manual survey-based sentiment collection with automated natural language processing systems that analyze communication content, metadata, and context data to determine customer sentiment objectively, eliminating human bias and incomplete feedback inherent in survey methods
Solution Approach 2:
The system introduces multiple intermediary components including NLP processors, metadata analyzers, and context data integrators that work together to transform raw communication data into accurate sentiment measurements, improving both precision and reliability through layered analysis
2Productivity
If automated sentiment analysis is applied to technical support communications, then real-time sentiment can be determined, but accuracy deteriorates due to challenges in formal interaction contexts
Solution Approach 1:
The patent segments the sentiment analysis process into distinct modules: communication content analysis, metadata processing, context data integration, and sentiment calculation. Each module handles specific aspects independently, improving accuracy by addressing formal interaction challenges at each stage rather than attempting holistic analysis
Solution Approach 2:
The system dynamically adjusts analysis parameters based on communication type, formality level, and context data. By changing weighting factors, feature importance, and model parameters according to the specific interaction context, the system maintains high accuracy across varied formal technical support scenarios while preserving real-time processing capability
3Measurement precision
If comprehensive context data is integrated into sentiment analysis, then sentiment determination accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex processing system into specialized components: communication content processors, metadata analyzers, context data repositories, and sentiment calculation engines. Each component has a specific function, reducing overall system complexity while enabling comprehensive analysis through modular architecture
Solution Approach 2:
The system performs preliminary processing and organization of context data, metadata, and communication content before sentiment analysis. By pre-processing and structuring data in advance, the system reduces the computational complexity during actual sentiment determination while maintaining comprehensive analysis capability
Data Source
AI summary
Systems and methods are described for determining customer sentiment using natural language processing in technical support communications. Communication content exchanged between a customer device and an agent device may be filtered to remove technical support syntax. Using natural language processing techniques, the processor may assign baseline values to features within the filtered communication content. To assign the baseline values, features from the filtered communication content may be identified, where the features pertain to expressed sentiments, and a trained first model may be applied to identify polarities and strengths related to the identified features. A score value may then be assigned to each identified feature, the score values being based on the polarities and strengths. A subset of the score values may then be weighted based on metadata and/or context, and the score values may be combined using a second model to determine an overall sentiment of the filtered communication content.